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Datasets:
zParik
/
MorVis

Tasks:
Image Classification
Size:
10K<n<100K
Tags:
malware-detection
3D-CNN
Morton-curve
cybersecurity
PE-executables
License:
Dataset card Files
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Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 91, in _split_generators
                  pa_table = next(iter(self._generate_tables(**splits[0].gen_kwargs, allow_full_read=False)))[1]
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 193, in _generate_tables
                  examples = [ujson_loads(line) for line in batch.splitlines()]
                              ^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/utils/json.py", line 20, in ujson_loads
                  return pd.io.json.ujson_loads(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
              ValueError: Expected object or value
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                         ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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MorVis: 3D Volumetric Malware Detection Tensors

Dataset Description

This dataset contains 6-channel 3D volumetric tensors (64×64×64) generated from Windows PE executables using Morton (Z-order) curve mapping. It accompanies the paper "3D Volumetric Malware Detection Using Morton Curves and Multi-Channel Semantic Features" (Harish et al., 2026).

Dataset Summary

  • Malware tensors: Generated from the VirusShare_00499 dump (~28,000 samples)
  • Benign tensors: Not included to save storage and download time (~150GB). Users can generate benign tensors using the provided script on their own installed applications.
  • Tensor shape: (6, 64, 64, 64) per sample, saved as .npy files
  • Curve order: 6 (64³ = 262,144 voxels)

Channels

Each tensor has 6 semantic channels:

Channel Name Description
0 Raw bytes Normalized byte values (0–1)
1 Entropy Local Shannon entropy over a sliding window
2 Code mask Binary mask for executable sections (.text, .code)
3 Import density Proximity to import/IAT tables (behavioral signal)
4 String density Fraction of printable ASCII in a local window
5 Data mask Binary mask: 1 = real file bytes, 0 = padding

Generation Script

malware_3d_multichannel.py is provided in this repository. Usage:

python malware_3d_multichannel.py -i ./samples -o ./tensors --order 6

Arguments:

  • --input_dir / -i: Directory containing PE files
  • --output_dir / -o: Output directory for .npy tensors
  • --order: Curve order (default: 6, giving 64³ grid)
  • --min_size: Minimum file size in KB (default: 10)
  • --max_size: Maximum file size in MB (default: 50)

The script parses PE headers, extracts relevant sections (skipping resources, relocations, debug), computes all 6 channels, maps bytes into 3D via Morton curve, and saves each tensor as a NumPy .npy file along with a metadata.json.

Generating Benign Tensors

Benign tensors are not hosted due to the prohibitive size (~150GB). To generate your own, run the script on locally installed applications:

python malware_3d_multichannel.py -i "C:\Windows\System32" -o ./tensors_benign
python malware_3d_multichannel.py -i "C:\Program Files" -o ./tensors_benign

Any directory containing legitimate PE executables will work.

Source Data

  • Malware: VirusShare_00499 dump (Windows PE executables)
  • Benign: User-installed Windows applications and system files

Citation

If you use this dataset, please cite (paper currently under review):

@article{harish2026morvis,
  title={3D Volumetric Malware Detection Using Morton Curves and Multi-Channel Semantic Features},
  author={Harish, Parikshieth and P.S., Ramesh and C, Suganthan},
  year={2026}
}

Authors

School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
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